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Few studies have explored meaning in life as a mediator between adverse childhood experiences (ACEs) and mental health. This study examined the protective and risk factors associated with meaning in life, as they mediate the effect of ACEs on mental health. The sample was 293 university students in Hong Kong (mean age = 21 years). The results of analyses based on three‐wave longitudinal data and structural equation modeling demonstrated that the presence of meaning mediated the negative association between ACEs and happiness, and that meaning confusion mediated the positive association between ACEs and depression and anxiety. This study provides evidence that the presence of meaning may play a protective role and that meaning confusion may be a risk factor for the effects of ACEs on mental health. This study's results have implications for the development of prevention and intervention strategies to alleviate the detrimental impact of ACEs and promote mental health in young adults. [ABSTRACT FROM AUTHOR]

This study explored diethanolammonium chloride ([DEA]Cl) as a potential ionic liquid from low‐cost and commercial ones for gas separation. There are three kinds of functional sites in [DEA]Cl: acidic NH2+, hydrogen‐bond OH, and nucleophilic Cl−. The separation of NH3/N2/H2, SO2/CO2/N2, and H2S/CO2/CH4 was considered, because the three mixtures are extensively involved in many industrial processes. The experimental results demonstrated that [DEA]Cl exhibited excellent ability to separate the mixture of NH3/N2/H2. The [DEA]Cl also exhibited the proper capacity to separate SO2/CO2/N2, but the capacity of [DEA]Cl for the separation of H2S/CO2/CH4 was inferior. The theoretical calculations revealed that NH2+ and OH worked synergistically to contribute to the ability of [DEA]Cl for excellent separation of NH3 from NH3/N2/H2, with Cl− contributing to the proper separation of SO2 from SO2/CO2/N2. This study provides experimental and theoretical basis for the industrial application of [DEA]Cl in the area of gas separation. [ABSTRACT FROM AUTHOR]

Purpose: To non‐invasively evaluate the Ki‐67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. Methods: A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual‐ and population‐level clustering. Handcrafted radiomics and deep learning‐based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. Results: Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429–0.727] vs. 0.559 [95% CI, 0.405–0.705], p‐value: 0.920; deep RSs: 0.670 [95% CI, 0.516–0.802] vs. 0.551 [95% CI, 0.397–0.698], p‐value: 0.776). The fusion RSs, combining handcrafted and deep learning‐based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714–0.900) and 0.792 (95% CI, 0.647–0.897) for the internal and external validation cohorts, respectively. Conclusions: The subregional radiomics approach can accurately predict the Ki‐67 level based on DBT data; thus, it may be used as a potential non‐invasive tool for preoperative treatment planning in BC. [ABSTRACT FROM AUTHOR]